Full-Stack Engineer

Company: Confidential - Seed-stage AI growth-software startup
Location: San Francisco, CA (in office Monday-Friday)
Compensation: $150,000 - $250,000 + 0.5% - 1.5% equity
Employment Type: Full-time
Visa Sponsorship: Not available (US citizens or current visa holders; no new H-1B)

About the Company

A seed-stage startup building self-improving software: AI that generates and tests new product experiences to lift revenue. Well-known consumer apps run their onboarding and paywalls on its SDK.

The Role

Our client is hiring two full-stack engineers to own major components of its AI Experience Generation product, which serves millions of users daily. You will build features in the Next.js frontend, ship scalable Go code serving thousands of requests per second, load-test servers as volume grows 10x, and design data systems and ML training infrastructure for automated experimentation. You will be given problems, not tickets, and will write your own specs, talk to customers and own features end to end, reporting to the CTO (ex-Alexa ML Infra).

What You Will Do

• Ship features on the Next.js / Go product serving tens of millions of users daily.
• Tune, optimize and load-test servers as volume grows toward hundreds of millions of users per day.
• Design data systems and ML training infrastructure for automated experimentation.
• Work with the founders and consumer-app customers to design and deliver new experiences.
• Own features end to end: write specs, ship, test and validate with real users.

What You Bring

• 3-7 years of software engineering with genuine full-stack range
• Backend experience (Python, Go or AWS infrastructure) and frontend experience (TypeScript/React or equivalent)
• One strong signal: 1.5+ years on a tier-1 engineering team, a founder background, a CS degree from a top school, or other evidence such as notable side projects or OSS
• Willingness to work in the San Francisco office Monday-Friday, with frequent weekend pushes
• US work authorization without new H-1B sponsorship

Nice to Have

• 0-to-1 product or feature ownership
• Early engineer at a startup (roughly #1-15)
• Production LLM work: prompting, RAG, agents, evals
• Data pipeline and workflow infrastructure
• Building in public: writing, OSS, talks

Benefits

All meals in office, gym membership, quarterly off-sites, and early access to the newest AI models.